Recent advances in neural radiance fields enable novel view synthesis of photo-realistic images in dynamic settings, which can be applied to scenarios with human animation. Commonly used implicit backbones to establish accurate models, however, require many input views and additional annotations such as human masks, UV maps and depth maps. In this work, we propose ParDy-Human (Parameterized Dynamic Human Avatar), a fully explicit approach to construct a digital avatar from as little as a single monocular sequence. ParDy-Human introduces parameter-driven dynamics into 3D Gaussian Splatting where 3D Gaussians are deformed by a human pose model to animate the avatar. Our method is composed of two parts: A first module that deforms canonical 3D Gaussians according to SMPL vertices and a consecutive module that further takes their designed joint encodings and predicts per Gaussian deformations to deal with dynamics beyond SMPL vertex deformations. Images are then synthesized by a rasterizer. ParDy-Human constitutes an explicit model for realistic dynamic human avatars which requires significantly fewer training views and images. Our avatars learning is free of additional annotations such as masks and can be trained with variable backgrounds while inferring full-resolution images efficiently even on consumer hardware. We provide experimental evidence to show that ParDy-Human outperforms state-of-the-art methods on ZJU-MoCap and THUman4.0 datasets both quantitatively and visually.
Deblur-Avatar: Animatable Avatars from Motion-Blurred Monocular Videos
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；S-Lab for Advanced Intelligence, Nanyang Technological University（南洋理工大学先进智能实验室）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；SenseTime Research（商汤科技研究院）
ETH Zürich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Tübingen（蒂宾根大学）
Combining human body models with differentiable rendering has recently enabled animatable avatars of clothed humans from sparse sets of multi-view RGB videos. While state-of-the-art approaches achieve realistic appearance with neural radiance fields (NeRF), the inferred geometry often lacks detail due to missing geometric constraints. Further, animating avatars in out-of-distribution poses is not yet possible because the mapping from observation space to canonical space does not generalize faithfully to unseen poses. In this work, we address these shortcomings and propose a model to create ani…
Interactive Rendering of Relightable and Animatable Gaussian Avatars
State Key Lab of CAD & CG, Zhejiang University（浙江大学计算机辅助设计与图形学国家重点实验室）；UCL Centre for Artificial Intelligence, Department of Computer Science, University College London（伦敦大学学院计算机科学系人工智能中心）；Kahlert School of Computing, University of Utah（犹他大学Kahlert计算学院）
One-shot Implicit Animatable Avatars with Model-based Priors
Zhejiang University（浙江大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Cambridge（剑桥大学）；Xiaohongshu Inc.（小红书公司）；Fullong Inc.（福龙公司）
ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Universität Freiburg（弗赖堡大学）；Saarbrücken Research Center for Visual Computing, Interaction and AI（萨尔布吕肯视觉计算、交互与人工智能研究中心）；ETH Zürich（苏黎世联邦理工学院）
ShanghaiTech University（上海科技大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）；Shanghai Engineering Research Center of Energy Efficient and Custom AI IC（上海能效与定制人工智能集成电路工程技术研究中心）
Modeling the human body in a canonical space is a common practice for capturing and animation. But when involving the neural radiance field (NeRF), learning a static NeRF in the canonical space is not enough because the lighting of the body changes when the person moves even though the scene lighting is constant. Previous methods alleviate the inconsistency of lighting by learning a per-frame embedding, but this operation does not generalize to unseen poses. Given that the lighting condition is static in the world space while the human body is consistent in the canonical space, we propose a du…
ETH Zürich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems, Tübingen（马克斯·普朗克智能系统研究所（蒂宾根））；University of Tübingen（蒂宾根大学）
In this paper, we aim to create generalizable and controllable neural signed distance fields (SDFs) that represent clothed humans from monocular depth observations. Recent advances in deep learning, especially neural implicit representations, have enabled human shape reconstruction and controllable avatar generation from different sensor inputs. However, to generate realistic cloth deformations from novel input poses, watertight meshes or dense full-body scans are usually needed as inputs. Furthermore, due to the difficulty of effectively modeling pose-dependent cloth deformations for diverse …
SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian Splatting
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Prometheus Vision Technology Co., Ltd.（天演智视科技有限公司）；The Hong Kong University of Science and Technology（香港科技大学）
3D-Consistent Human Avatars with Sparse Inputs via Gaussian Splatting and Contrastive Learning
School of Computer Science, Wuhan University（武汉大学计算机学院）；AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）；Huazhong University of Science and Technology（华中科技大学）；Wuhan National Laboratory for Optoelectronics（武汉光电国家实验室）
We present animatable neural radiance fields (animatable NeRF) for detailed human avatar creation from monocular videos. Our approach extends neural radiance fields (NeRF) to the dynamic scenes with human movements via introducing explicit pose-guided deformation while learning the scene representation network. In particular, we estimate the human pose for each frame and learn a constant canonical space for the detailed human template, which enables natural shape deformation from the observation space to the canonical space under the explicit control of the pose parameters. To compensate for i…
Facebook Reality Labs（Facebook现实实验室）；University of Southern California（南加州大学）
In this paper, we propose ARCH (Animatable Reconstruction of Clothed Humans), a novel end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image. Existing approaches to digitize 3D humans struggle to handle pose variations and recover details. Also, they do not produce models that are animation ready. In contrast, ARCH is a learned pose-aware model that produces detailed 3D rigged full-body human avatars from a single unconstrained RGB image. A Semantic Space and a Semantic Deformation Field are created using a parametric 3D body estimator. The…
Structure-Aware Fine-Grained Gaussian Splatting for Expressive Avatar Reconstruction
School of Cyber Science and Engineering, Southeast University（东南大学网络空间安全学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education（教育部新一代人工智能技术及其跨学科应用重点实验室（东南大学））；Purple Mountain Laboratories（紫金山实验室）；Engineering Research Center of Blockchain Application, Supervision And Management (Southeast University), Ministry of Education（教育部区块链应用监管工程研究中心（东南大学））；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS)（多模态人工智能系统全国重点实验室）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Topology-aware Human Avatars with Semantically-guided Gaussian Splatting
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Software Engineering, Xi’an Jiao Tong University（西安交通大学软件学院）；AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）；Huazhong University of Science and Technology（华中科技大学）
